Here one aligns existing database records with the sentences in which these records have been "rendered"--effectively labeling the text-and from this labeling we can train a machine learning system as before (Craven and Kumlien, 1999;Mintz et al., 2009;Bunescu and Mooney, 2007;Riedel et al., 2010).

this method relies on the availability of a large database that has the desired schema.


This is similar in spirit to work on learning entailment rules (Szpektor et al., 2004;Zanzotto et al., 2006;Szpektor and Dagan, 2008).

for us even entailment rules are just a by-product of our goal to improve prediction, and it is this goal we directly optimize for and evaluate.


Methods that learn rules between textual patterns in OpenIE aim at a similar goal as our proposed approach (Schoenmackers et al., 2008;Schoenmackers et al., 2010).

their approach is substantially more complex, requires a categorization of entities into fine grained entity types, and needs inference in high tree-width Markov Networks.


By contrast, for a surface pattern like "X visits Y" X could be a person or organization, and Y could be a location, organization or person.

in terms of MAP score this time there is no obvious winner among the latent models.


The rich get richer dynamic of the DP leads to the use of a compact set of rules that is an effective feature set for NLI (Swanson and Charniak, 2012).

this same property makes rare rules harder to find.


It is speculative to assign causes to the discriminative rules we report, and we leave quantification of such statements to future work.

the strength of the signal, as evidenced by actual counts in data, and the high level interpretation that can be easily assigned to the TSG rules is promising.


For an adult speaker of a language, word segmentation from fluid speech may seem so easy that it barely needed to be learned.

pauses in speech and word boundaries are not well correlated (Cole & Jakimik, 1980), word boundaries are marked by a conspiracy of partially-informative cues (Johnson & Jusczyk, 2001), and different languages mark their boundaries differently (Cutler & Carter, 1987).


This results in a 22081word corpus, 10% fewer tokens than in the original.

it does not substantially change the lexicon; the number of distinct word types only drops from 811 to 806.


In all four languages, we see a similar picture: the shared features approach is generally better when one of the treebanks is very small, while the guided parsing approach is better when the treebanks are more similar in size.

for most training set sizes the combination of the the two methods achieves a higher performance than either of them individually.


the treebank annotation style) is different.

domain adaptation and cross-treebank training can be seen as instances of the more general problem of multitask learning (Caruana, 1997).


In this work, f 1 , f 2 , and f s are identical: all of them correspond to the feature set described by Carreras (2007).

it is certainly imaginable that f s could consist of specially tailored features that make generalization easier.


Since the Talbanken treebank is twice as large as the Syntag treebank and has a surface-oriented representation that is easier to parse, this parser is useful as a guide for the Syntag parser: the improvements of the guided and combined Syntag parsers are statistically significant.

it is harder to improve the Talbanken parser, for which the baseline is much stronger.


Toutanova and Johnson (2008) (also, Ravi and Knight (2009)) use a simple method for predicting possible tags for unknown words: a set of 100 most common suffixes are extracted and then models of P(tag|suffix) are built and applied to unknown words.

these models suffer with an extremely small set of labeled data.


(2010) that good bigrams are those which have high coverage of new words: each newly covered node contributes additional (partial) counts.

by using the weights instead of full counts, we also account for the confidence assigned by LP.


In an unsupervised setting, there is no reason f-LDA would actually infer parameters corresponding to the three factors we have been describing.

the forums include metadata that can help guide the model: the messages are organized into forums corresponding to drug type (factor 1), and some threads  are tagged with labels corresponding to routes of administration and other aspects (factors 2 and 3).


One option is to fix ω as η, forcing the component weights to match the provided weights.

in our case η will only be an approximation of the optimal component parameters since it is estimated from incomplete data (only some messages have tags) and the η vectors are learned using an approximate model (see below).


This is a two-dimensional model, since we explicitly model pairs such as (MEPHEDRONE,SNORTING) or (SALVIA,EFFECTS).

we also created word distributions for triples such as (SALVIA,ORAL,EFFECTS) by taking a mixture of the corresponding pairs: in this example, we estimate the unigram distribution from salvia documents tagged with either "Oral" or "Effects."


There are certainly some differences between our training set and our phrases.

the collected training samples was the closest available dataset to our purpose.


The metric described above only considers word overlap and ignores other semantic relations (e.g., synonymy, hypernymy) between words.

annotators write labels of their own and may use words that are not directly from the conversation but are semantically related.


On the blog corpus, our key phrase extraction method (Extraction-BL) fails to beat the other baselines (Lead-BL and Freq-BL) in majority of cases (except R-f1 for Lead-BL).

in the email dataset, it improves the performance over both baselines in both evaluation metrics.


Incorporating a better source of prior knowledge in the generalization phase (e.g., YAGO or DBpedia) is also an interesting research direction towards a better phrase aggregation step.

we plan to apply a ranking strategy to select the top candidate phrases generated by our framework.


In this paper, we describe an improved method for combining partial captions into a final output based on weighted A * search and multiple sequence alignment (MSA).

to prior work, our method allows the tradeoff between accuracy and speed to be tuned, and provides formal error bounds.


Most of the previous research on real-time captioning has focused on Automated Speech Recognition (ASR) (Saraclar et al., 2002;Cooke et al., 2001;Pražák et al., 2012).

experiments show that ASR systems are not robust enough to be applied for arbitrary speakers and in noisy environments (Wald, 2006b;Wald, 2006a;Bain et al., 2005;Bain et al., 2012;Cooke et al., 2001).


The algorithm is designed to be used online, and hence has high speed and low latency.

due to the incremental nature of the algorithm and due to the lack of a principled objective function, it is not guaranteed to find the globally optimal alignment for the captions.


Despite the significant reduction in the search space, the A * search may still need to explore a large number of nodes, and may become too slow for real-time captioning.

we can further improve the speed by following the idea of weighted A * search (Pohl, 1970).


WER has several nice properties such as: 1) it is easy to estimate, and 2) it tries to preserve word ordering.

wER does not account for the overall 'readability' of text and thus does not always correlate well with human evaluation (wang et al., 2003;He et al., 2011).


The widely-used BLEU metric has been shown to agree well with human judgment for evaluating translation quality (Papineni et al., 2002).

unlike WER, BLEU imposes no explicit constraints on the word ordering.


The algorithm can be tailored to real-time by using a larger heuristic weight.

we can produce better transcripts for offline tasks by choosing a smaller weight.


We evaluated our models on a set of retellings from 70 non-overlapping subjects with a mean age of 88.5, half of whom had received a diagnosis of MCI (test set).

to the unsupervised word-alignment based method, the method outlined here required manual story element labels of the retellings.


In offline scenarios where high latencies are permitted, several adaptation strategies (speaker, language model, translation model), denser data structures (Nbest lists, word sausages, lattices) and rescoring procedures can be utilized to improve the quality of end-to-end translation.

realtime speech-to-text or speech-to-speech translation demand the best possible accuracy at low latencies such that communication is not hindered due to potential delay in processing.


Ideally, one would like to train the models on entire talks.

such corpora are not available in large amounts.


We leverage the IWSLT TED campaign by using identical development (dev2010) and test data (tst2010).

english-Spanish is our target language pair as our internal projects are cater mostly to this pair.


For example, the input text we use will be chunked into the NC we and the VC use, which will be translated incorrectly as nosotros•usar; the infinitive usar is se-lected rather than the properly conjugated form usamos.

there is a marked improvement in translation accuracy with increasingly larger chunk sizes (lgchunk1, lgchunk2, and lgchunk3).


The non-linguistic segmentation using fixed word length windows also performs well, especially for the longer length windows.

longer windows (win15) increase the latency and any fixed length window typically destroys the semantic context.


Possible fixes to this problem include using a proper sentence level metric such a METEOR (Denkowski and Lavie, 2011) or a pseudo-corpus from the last few updates (Chiang et al., 2008).

in light of the result from section 3.1 that tuning on the dev set is still better than tuning on a held-out portion of the training data, we observe that tuning a corpus level metric on a highquality dev set from the same domain as the test set probably leads to the best translation quality.


Even someone who does not completely understand them can gain some meaning by observing common words such as "Oracle", "database" and "programming".

checking the Google results for "Oracle Database" or "programming languages", we will find little relatedness between them and C p .


It achieves 19.8% by micro-averaged F1 score, ranking 11th out of the 19 systems submitted to the competition (Kim et al., 2010).

by adding the structural features used by HUMB into CTI, we can improve the performance by around 6%, making our results close to that of HUMB.


However, this will not effect our method, because what it essentially measures is a term's informativeness among a list of terms appearing in the same context.

for keyword extraction, a topic with a rich literature, to the best of our knowledge, has no publicly available large scale datasets, which makes SemEval2010 the best available.


Here, we concentrate on developing and evaluating automatic procedures to learn the main concepts of a domain and at the same time auto-annotate texts so that they become available for training information extraction or text summarization applications.

it would be naive to think that in the current state of the art we would be able to learn all knowledge from text automatically (Poon and Domingos, 2010;Biemann, 2005;Buitelaar and Magnini, 2005).


We have found that the clustering-based procedure is very competitive when presented with gold chunks.

the iterative learning procedure performs very well when presented with automatic chunks in all tested domains and the two languages.


score(k) = w∈k TextRank(w) length(k) + 1 The small vocabulary size as well as the high redundancy within the set of related sentences are two factors that make keyphrase extraction easier to achieve.

a large number of the generated keyphrases are redundant.


It was shown to correlate significantly with human judgments (Clarke and Lapata, 2006) and behave similarly to BLEU (Unno et al., 2006).

this metric is not entirely reliable as it depends on parser accuracy and the type of dependency relations used (Napoles et al., 2011).


This model learns the cost of putting a word immediately before another word and finds the best reordering by solving an instance of the Traveling Salesman Problem (TSP).

for efficiently solving the TSP, the model is restricted to pairwise features which examine only a pair of words and their neighborhood.


If the exact segmentation of the source sentence was known, then the model could have used the information that the word gaadi {car} appears in a segment whose head is the noun chaalak {driver} and hence its not unusual to put gaadi {car} before bola {said} (because the construct "NP said" is not unusual).

since the segmentation of the source sentence is not known in advance, we use a heuristic (explained later) to find the segmentation induced by a reordering.


(2011) used only manually aligned data for training the TSP model.

we use machine aligned data in addition to manually aligned data for training the TSP model as it leads to better performance.


Translation models in statistical machine translation can be scaled to large corpora and arbitrarily-long phrases by looking up translations of source phrases "on the fly" in an indexed parallel corpus using suffix arrays.

this can be slow because on-demand extraction of phrase tables is computationally expensive.


The feature parameters Φ can be roughly divided into two categories: dense feature that measures the plausibility of each translation rule from a particular aspect, e.g., the rule translation probabilities p(f |e) and p(e|f ); and sparse feature that fires when certain phenomena is observed, e.g., when a frequent word pair co-occured in a rule.

to λ, feature parameters in Φ are usually modeled by generative models for dense features, or by indicator functions for sparse ones.


An alternative approach is to use log-linear interpolation, so that the interpolation weights can be easily optimised in tuning Bertoldi and Federico, 2009;Banerjee et al., 2011).

this effectively multiplies the probabilities across phrase tables, which does not seem appropriate, especially for phrases absent from 1 table.


All of the NLP challenges of MSA (e.g., optional diacritics and spelling inconsistency) are shared by DA.

the lack of standard orthographies for the dialects and their numerous varieties pose new challenges.


We did not use a language model to pick the best path; instead we kept the ambiguity in the lattice and passed it to our SMT system.

in this paper, we run ELISSA on untokenized Arabic, we use feature, lemma, and surface form transfer rules, and we pick the best path of the generated MSA lattice through a language model.


The Penn Treebank is composed of professionally-written news text from 1989, when minorities comprised 7.5% of the print journalism workforce; the proportion of women in the journalism workforce was first recorded in 1999, when it was 37% (American Society of Newspaper Editors, 1999).

twitter users in the USA contain an equal proportion of men and women, and a higher proportion of young adults and minorities than in the population as a whole (Smith and Brewer, 2012).


The internal differences within these social media -at least as measured by the distinctions drawn in Table 2 -are much smaller than the differences between these corpora and the PTB standard.

in the long run, the effectiveness of this approach will be limited, as it is clear from Figure 1 that social media is a moving target.


Online learning algorithms such as perceptron and MIRA have become popular for many NLP tasks thanks to their simpler architecture and faster convergence over batch learning methods.

while batch learning such as CRF is easily parallelizable, online learning is much harder to parallelize: previous efforts often witness a decrease in the converged accuracy, and the speedup is typically very small (∼3) even with many (10+) processors.


(2012) still requires 5-6 hours to train a very fast parser.

with the increasing popularity of multicore and cluster computers, there is a growing interest in speeding up training via parallelization.


Dot product both sides of Equation 2 with unit oracle vector u: 2.

from the two bounds we have: thus within at most t ≤ R 2 /δ 2 minibatch updates MIRA will converge.


Similar intuitions have been used to motivate the acquisition of bilexical features from background corpora for improving parser accuracy.

previous work has focused on including these statistics as auxiliary features during supervised training.


(2011) extracted ngram counts from Google queries and a large corpus to improve the MSTParser.

to previous work, we refer to our approach as self-learning because it differs from self-training by utilising statistics found using an initial parse ranking model to create a separate unsupervised reranking component, without retraining the baseline unlexicalised model.


Finally, we know that w 1 and w 2 are in a sentence together but cannot assume that there is a dependency relation between them.

we can choose to think of each sentence as a fully connected graph, with an edge going from every lemma to every other lemma in the same sentence.


While their work also combines sentiment analysis with collaborative filtering, the purpose is to improve the accuracy of item recommendation.

we borrow the idea and technique of collaborative filtering to improve user relation mining from online text.


The S-Joint model performs significantly better, correctly guessing 2-3 items for each output avatar.

as we will see in the manual evaluation, many of the non-matching parts it produces are still a good fit for the query.


To obtain the supertagged dependency linkage, the most intuitive way is through a LTAG parser (Schabes et al., 1988).

this could be very slow as it has time complexity of O(n 6 ).


The PR algorithm limits the length of the summary to approximately 140 characters, the maximum length of a Twitter post.

often the summary sentence produced has extraneous parts that appear due to the fact that they appear frequently in the posts being summarized, but these parts make the summary malformed or too wordy.


For example, to create parallel Chinese-English training texts for translation of social media texts, it takes three minutes on average to translate an informally written social media text of eleven words from Chinese into English.

it takes thirty seconds to normalize the same message, a six-fold increase in speed.


Most previous work on normalization of social media text focused on word substitution (Beaufort et al., 2010;Gouws et al., 2011;Han and Baldwin, 2011;Liu et al., 2012).

we argue that some other normalization operations besides word substitution are also critical for subsequent natural language processing (NLP) applications, such as missing word recovery (e.g., zero pronouns) and punctuation correction.


All the above work focused on normalizing words.

our work also performs other normalization operations such as missing word recovery and punctuation correction, to further improve machine translation.


We manually analyzed the effect of our text normalization decoder on MT.

for example, given the un-normalized English test message "yeah must sign up , im in lt25", our English-Chinese MT system translated it into "对[yeah] 必须[must] 签 署[sign up] ， im 在[in] lt25" our normalization decoder normalized it into "yeah must sign up , i 'm in lt25 ."


(2003) was the first to evaluate a model trained on incorrect usage as well as artificial errors for the task of correcting several different error types, including prepositions.

with limited training data, system performance was quite poor.


On a test set with few corrections (NUCLE), training on welledited text (and without using thresholds) performs particularly poorly.

when evaluating on the FCE test set which contains far more errors, training on well-edited text performs reasonably well (though statistically significantly worse than training on all of the Wikipedia errors).


This approach significantly increases the number of entries in the ReverseDictionary.


For the extraction of gradable adjectives, we rely, on the one hand, on the part-of-speech labels JJR (comparative) and JJS (superlative).

we also consider adjectives being modified by either more or most.


We also experimented with other related weaklysupervised extraction methods, such as mutual information of two adjectives at the sentence level (or even smaller window sizes).

using conjunctions largely outperformed these alternative approaches so we only pursue conjunctions here.


Since they occur very frequently, one might exclude some of them by just ignoring the most frequent adjectives.

there are also other types of adjectives, especially pertainyms (political, federal, economic, public, American, foreign, local, military, financial and national) that appear on this list which could not be excluded by that heuristic.


Argument-Argument Reordering estimates the reordering probability between two arguments, i.e., argument-argument pattern on the source side (AA-S) and its counterpart on the target side (AA-T).

due to that arguments are driven and pivoted by their predicates, we also include predicate in patterns of AA-S and AA-T. Let's revisit Figure 2(a).


(2009; presented models that learn phrase boundaries from aligned dataset.

semantics motivated SMT has also seen an increase in activity recently.


As  stressed, using derivation tree fragments allows the comparison to abstract away from interference by irrelevant modifiers, an issue with Dickinson and Meurers (2003).

in the context of IAA, this advantage of KBM plays out in a different way, in that it allows for a precise pinpointing of the inconsistencies.


First, the choice in sense inventory plays an important role in gathering high-quality annotations; fine-grained inventories such as WordNet often contain several related senses for polysemous words, which untrained annotators find difficult to correctly apply in a given context (Chugur et al., 2002;McCarthy, 2006;Palmer et al., 2007;Rumshisky and Batiukova, 2008;Brown et al., 2010).

many agreement studies have restricted annotators to using a single sense, which can significantly lower inter-annotator agreement (IAA) in the presence of ambiguous or polysemous usages; indeed, multiple studies have shown that when allowed, annotators readily assign multiple senses to a single usage (Véronis, 1998;Murray and Green, 2004;Erk et al., 2009;Passonneau et al., 2012b).


If the third annotator agrees with either of the first two, the instance is marked as a case of agreement.

the unadjudicated agreement for the dataset was 67.3 measured using pair-wise agreement.


Compound features are difficult to build on dense embeddings.

they are easy to induce from the sparse embedding clusters proposed in this paper.


Moreover, in the PARADISE framework, only quality measurement for the whole dialogue (or system) is allowed.

this is not suitable for using quality information for online adaption of the dialogue (cf.


Only users can give a rating about their satisfaction level, i.e., how they like the system and the interaction with the system.

user ratings are expensive as elaborated in Section 1.


The time complexity of LVM training has been addressed through parallel training algorithms (Wolfe et al., 2008;Chu et al., 2006;Das et al., 2007;Newman et al., 2009;Ahmed et al., 2012;Asuncion et al., 2011), which reduce LVM training time through the use of large computational clusters.

the memory cost for training LVMs remains a bottleneck.


Assigning the dissimilar documents earlier helps ensure that more greedy node selections are informed by these impactful assignments.

dISSIMILARITY has a prohibitive time complexity of O(T N 2 ), because we must compare T nodes to an order of N documents for a total of N iterations.


It is also important to consider the additional time required to execute the partitioning methods themselves.

in practice this additional time is negligible.


Zhu & Ibarra (1999) present theoretical results and propose techniques for the general partitioning task we address.

to that work, we focus on the case where the data to be partitioned is a large corpus of text.


These corrections include ( / / / A/Â/Ǎ/Ā), / y/ý and / h/h transformations.

mADA ARZ , as a codafication technique, uses the context of the word, which makes it a contextual modeling approach unlike CEC and mLE.


The lower MT scores and slower learning curve of the MTurk systems are both due to the lower quality of the translations, and to the mismatch with the professional development set translations (we discuss this issue further in §4.3).

by interpolation of the MT scores, we find that the same MT performance can be obtained by using twice the amount of MTurk translated data as professional data.


In theories stretching back to Karttunen (1976), indefinites function primarily to establish new discourse entities, and should be able to participate in coreference chains, but here the association with such chains is negative.

interactions explain this fact (see Table 4 and our discussion of it).


Each syllablesize segment contains the representation of exactly one vowel phoneme, so that the number of segments matches the number of syllables.

1 the hyphenation need not correspond exactly to the actual syllable breaks.


For a syllabification to be accepted, all its syllables must satisfy the four constraints.

if this results in rejection of all possible syllabifications, the constraints are gradually relaxed starting from the weakest.


If we had a sufficiently large training set of pronunciation-respelling pairs, we could train a machine learning algorithm to directly generate respellings for any strings of English phonemes.

such a training set is not readily available.


It is similar to to the phonemic respelling approach described in Section 4.3 in that it converts each phoneme to a letter sequence.

the mappings depend on adjacent phonemes, as well as on the CV pattern of the current syllable.


The main difference between the L2P model described in this section and the P2L model from Section 5.2 is that the input and output data are reversed.

the L2P model is not simply a mirror image of the P2L model.


Neither the context-sensitive respeller nor dictionary lookup seem to contribute much to eSpeak's performance.

disabling the P2L generator produces a significant drop in word accuracy, while removing the L2P correctness filter almost doubles the phoneme error rate.


We then calculate pairwise accuracy as the percentage of pairs w p , w n (w p ∈ P seeds and w n ∈ N seeds ) where PN w p > PN w n .

this metric does not address the case where the degree of a word in one stylistic dimension is overestimated because of its status on a parallel dimension.


These algorithms use examples from each domain to learn a general model that is also sensitive to individual domain differences.

many data sets include a host of metadata attributes, many of which can potentially define the domains to use.


The use of the word "slew", in this case, has negative connotations, but only if the whole statement is construed by the perspective of the reader to represent an opinion.

if Lloyd Hession is a provider of new network access control solutions, then the use of "open" may convert this negative context into a positive context.


While these types of heuristics support longer-distance syntactic relations, they tend to focus on cases where some form of semantic compositionality holds.

consider this sentence from the IT business press: The contract is consistent with the desktop computing Outsourcing deals Citibank awarded EDS and Digital Equipment in 1996. .


The reason f f ac outperforms f SC is that f SC primarily controls for over-coverage of any element not in the subset via the α saturation hyper-parameter.

it does not ensure that every non-selected element has good representation in the subset.


While the previous studies use in-domain data sets for simulation, it is quite common to collect large amounts of OOD text data from the web.

given the nature of web data, some kind of selection mechanism is needed to ensure quality.


Doing the same rank-based comparison for the CMs this time, we observe that the syllable and morph-based models have the same average rank of 1.5, whereas the word-based model has 2.8.

a closer look reveals that the syllable-based CM paired with NO-LM is an outlier because NO-LM approach allows variety at the output but when the unit of the confusion model is as small as syllables, it produces too much variety that deterio-rates the discriminative model.


IB1 achieves an optimal ranking after just one iteration and thereafter scores get worse due to semantic drift.

pruning helps avoid semantic drift for IB2, which attains an optimal score after 2 iterations and achieves relatively constant scores for several iterations.


In contrast, pruning helps avoid semantic drift for IB2, which attains an optimal score after 2 iterations and achieves relatively constant scores for several iterations.

during iteration 9 an incorrect pattern is kept and this at once leads to a drastic loss in accuracy, showing that semantic drift is only deferred and not completely eliminated.


In other words, iterative bootstrapping is not benefitting from the information provided by the additional labeled data, and thus has a poor learning performance.

for our method based on hitting times, the performance continually improves as the seed set size is increased.


In such a tagging scheme, indi-vidual binary classifiers for each tag are independent of one another.

recent studies have found merit in segmenting each message into functional units and assigning a single DA to each segment (Hu et al., 2009).


That work was also not capable of learning from supervised annotations in a downstream task.

our approach uses document-level sentiment annotations to learn about the role of discourse connectors in sentence-level subjectivity.


Discourse coherence has always been used as a key metric in human scoring rubrics for various assessments of spoken language.

very little research has been done to assess a speaker's coherence in automated speech scoring systems.


These features were either extracted directly from the speech signal or were based on the output of an automatic speech recognition system (with a word error rate of around 28% 1 1 Both the training and evaluation sets used to develop the speech recognizer consist of similar spoken responses drawn from the same assessment.

there is no response overlap between these sets and the corpus used for discourse coherence annotation in this study.


The mismatch in the level of abstraction between the natural language representation and the regular expression representation make this a novel and challenging problem.

a given regular expression can be written in many semantically equivalent forms, and we exploit this flexibility to facilitate translation by finding a form which more directly corresponds to the natural language.


1 Regular expressions (regexps) have proven themselves to be an extremely powerful and versatile formalism that has made its way into everything from spreadsheets to databases.

despite their usefulness and wide availability, they are still considered a dark art that even many programmers do not fully understand (Friedl, 2006).


utilize unification to find possible ways to decompose the logical form.

they perform only syntactic unification.


The DFA representation of a regular expression may be exponentially larger than the the original regular expression.

past work has shown that most regular expressions do not exhibit this exponential behavior (Tabakov and Vardi, 2005;Moreira and Reis, 2012), and the conversion process is renowned for its good performance in practice (Moreira and Reis, 2012).


We define the features in our model over individual parse productions, admitting the use of dynamic programming to efficiently calculate the unconditioned expected counts.

when we condition on generating the correct regular expression, as in the first term in (2), the calculation no longer factorizes, rendering exact algorithms computationally infeasible.


Thus, they approximate this full computation using beam search, maintaining only the m-best logical forms at each chart cell.

qualitatively, our n-best approximation always represents the most likely parses in the approximation, but the number of represented parses scales only linearly with n. the number of parses represented by the beam search algorithm of the past work can potentially scale exponentially with the beam size, m, due to its use of dynamic programming.


One solution is to adopt a nonparametric Bayesian method by incorporating a hierarchical prior over the parameters (e.g., a Dirichlet process).

this approach can impose unrealistic restrictions on the model choice and result in intractability which requires sampling or approximate inference to overcome.


Let ρ w = The similarity of some usage kets |u is obtained, as is common in the literature, by their inner product u j .

as this is a complex value, we multiply it with its complex conjugate, rendering the real value u Therefore, in total the expected similarity of w and v is: We see that the similarity function simply reduces to multiplying ρ w with ρ v and applying the trace function.


Most recent Open IE systems have targeted verbal relations (Banko et al., 2007;Mausam et al., 2012), claiming that these are the majority.

chan and Dan (2011) show that only 20% of relations in the AcE programs Relation Detection and characterization (RDc) are verbal.


Typically an Open IE system is tested on one dataset.

because the definition of relation is ambiguous, we believe that is necessary to test with multiple datasets.


A matching component that uses the phrases "New York," "day care," and "license" is likely to do better.

a better matching component will understand that in the context of this query all three phrases "New York," "day care" and "license" are important, and that "New York" needs to modify "day care."


Since these papers considered a small number of answer types, rules over the detected relations and answer types could be applied to find the relevant answer.

since our system answers non-factoid questions that can have answer of arbitrary types, we want to use as few rules as possible.


These studies focused on the exterior characteristics of spoken genres, since they assumed that entire scripts are given in advance and then they extracted keywords that best describe the scripts.

to the best of our knowledge, there is no previous study considered time of utterances which is an intrinsic element of spoken genres.


information decreases gradually by the exponential nature of forgetting.

whenever the information is repeated, it is recalled longer.


Existing ontologies are not optimal for solving opaque coreferent mentions because of both a precision and a recall problem (Lee et al., 2011;Uryupina et al., 2011).

using data-driven methods such as distributional semantics for coreference resolution suffers especially from a precision problem (Ng, 2007).


There are many similarities between paraphrase and coreference, and our work is most similar to that by Wang and Callison-Burch (2011).

some paraphrases that might not be considered to be valid (e.g., under $200 and around $200) can be acceptable coreference relations.


We then develop novel semantic, syntactic and salience features for this task, showing strong improvements over one of the best known prior models (Poesio et al., 2004).

this local model classifies each anaphor-antecedent candidate pair in isolation.


There is a partial overlap between bridging and implicit noun roles (Ruppenhofer et al., 2010).

work on implicit noun roles is mostly focused on few predicates (e.g.


• Bridging is a relatively local phenomenon with 71% of NP antecedents occurring in the same or up to 2 sentences prior to the anaphor.

farther away antecedents are common when the antecedent is the global focus of a document.


The salience feature in the pairwise model only measures the salience for candidates within the local window.

globally salient antecedents are preferred even if they are far away from the anaphor.


Recall that existing approaches have relied primarily on morphosyntactic features as well as a few semantic features extracted from WordNet synsets and VerbOcean's (Chklovski and Pantel, 2004) semantic relations.

we propose not only novel lexical and grammatical features, but also sophisticated features involving semantics and discourse.


Hence, unlike syntactic dependencies and predicateargument relations through which we can identify intra-sentential temporal relations, discourse relations can potentially be exploited to discover both inter-sentential and intra-sentential temporal relations.

no recent work has attempted to use discourse relations for temporal relation clas-(6) { Arg1 Hewlett-Packard Co. said it raised its stake in Octel Communications Corp. to 8.5% of the common shares outstanding.


The rightmost three columns correspond to the three ways of combining rules and machine learning described in Section 4.3.

the rows of the table differ in terms of what features are available to a system.


At most, one might think that reordering is lexicalized-perhaps, (for instance) in translating from Chinese to English, or from Arabic to English, there are certain words whose English translations tend to undergo long-distance movement from their original positions, while others stay close to their original positions.

one would not expect a particular Chinese adverb or a particular Arabic noun to undergo long-distance movement when being translated into English in one domain, but not in others.


Many SMT systems, including our own, still use this distance-based penalty as a feature.

starting with (Tillmann and Zhang, 2005;Koehn et al., 2005), a more sophisticated type of reordering model has often been adopted as well, and has yielded consistent performance gains.


The resulting mixture model is deficient (non-normalized), but easy to fix by backing off to a global distribution such as p(o) in equation 1.

we found that this "fix" caused large drops in performance, for instance from the Arabic BLEU score of 48.3 reported in table 3 to 46.0.


Several alternatives now exist: MIRA (Watanabe et al., 2007;Chiang et al., 2008), PRO (Hopkins and May, 2011), linear regression (Bazrafshan et al., 2012) and ORO (Watanabe, 2012) among others.

these approaches optimize towards the best score as reported by a single evaluation metric.


In (Duh et al., 2012) the choice of which option to use rests with the MT system developer and in that sense their approach is an a posteriori method to specify the preference (Marler and Arora, 2004).

to this, our tuning framework provides a principled way of using the Pareto optimal options using ensemble decoding .


Research in SMT parameter tuning has seen a surge of interest recently, including online/batch learning (Watanabe, 2012;Cherry and Foster, 2012), large-scale training (Simianer et al., 2012;He and Deng, 2012), and new discriminative objectives (Gimpel and Smith, 2012;Zheng et al., 2012;Bazrafshan et al., 2012).

few works have investigated the multi-metric tuning problem in depth.


This is advantageous because the optimal weighting may not be known in advance.

the notion of Pareto optimality implies that multiple "best" solutions may exist, so the MT system developer may be forced to make a choice after tuning.


inal PMO-PRO seeks to maximize the points on the Pareto frontier (blue curve in the figure) leading to Pareto-optimal solutions.

the PMO ensemble combines the different Paretooptimal solutions and potentially moving in the direction of dashed (green) arrows to some point that has higher score in either or both dimensions.


This has been the general motivation for considering the linear combination of metrics (Cer et al., 2010;Servan and Schwenk, 2011) resulting in a joint metric, which is then optimized.

due to the scaling differences between the scores of different metrics, the linear combination might completely suppress the metric having scores in the lower-range.


All the proposed methods fit naturally within the usual SMT tuning framework.

some changes are required in the decoder to support ensemble decoding and in the tuning scripts for optimizing with multiple metrics.


Figures 3(b) and 4(a) show that none of the proposed methods managed to improve the baseline scores for METEOR and TER.

several of our ensemble tuning combinations work well for both ME-TEOR (BR, BMRTB3, etc.)


In the popular cube pruning algorithm, every hypothesis is annotated with boundary words and permitted to recombine only if all boundary words are equal.

many hypotheses share some, but not all, boundary words.


Many features are additive: they can be expressed as weights on edges that sum to form hypothesis features.

log probability from an N -gram language model is non-additive because it examines surface strings across edge and vertex boundaries.


Their work bears some similarity to our algorithm in that partially overlapping state will be collapsed and efficiently handled together.

the key advatage to our approach is that groups have a score that can be used for pruning before the group is expanded, enabling pruning without first constructing the intersected automaton.


There are several forms of coarse-to-fine search; the closest to our work increases the language model order each iteration.

by operating inside search, our algorithm is able to handle hypotheses at different levels of refinement and use scores to choose where to further refine hypotheses.


The clusters that have no member noun were hidden from the ranking since they do not explicitly represent any concept.

these clusters are still part of the organisation of conceptual space within the model and they contribute to the probability for the clusters on upper levels (eq.


Another important reason AGG fails is that it by definition organises all concepts into tree and optimises its solution locally, taking into account a small number of clusters at a time.

being able to discover connections between more distant domains and optimising globally over all concepts is crucial for metaphor identification.


We manually compiled sets of typical features for the 10 source domains, and measured their recall among the top 50 HGFC features at R = 0.70.

in practice the coverage in this task would directly depend on that of the metaphorical associations.


Across all documents, a perfect agreement was never achieved, which confirms the difficulty of annotating such a subjective task: The average lccm per document on the manual annotations is 0.56 (dense chains), respectively 0.54 (merged chains).

the considerable overlap between the annotators still enables us to evaluate automatic chaining methods, and the lccm agreement score serves as an upper bound.


The former outperforms the previous best system UTD N B in both Spearman's ρ and MaxDiff accuracy, where the differences are statistically significant 8 ; the latter has comparable performance, where the differences are not statistically significant.

while the IsA relation from Probase is exceptionally good in identifying Class-Inclusion relations, with high Spearman's ρ = 0.619 and MaxDiff accuracy 8 We conducted a paired-t test on the results of each of the 69 relation.


Notice that the main purpose of the ablation study is to verify the importance of an individual component model when a significant performance drop is observed after removing it.

occasionally the overall performance may go up slightly.


Lexical features are important as they can capture semantic meaning precisely.

given that we do not have many labeled examples, lexical features can lead to overfitting.


The same authors proposed an unsupervised method which relies on associations of aspects with topics indicative of stances mined from the Web for the task (Somasundaran and Wiebe, 2009).

our model is also an unsupervised one but we do not rely on any external knowledge.


Recently, Mukherjee and Liu (2012) proposed an unsupervised model to extract different types of expressions including agreement/disagreement expressions.

our focus is not to detect agreement/disagreement expressions but to model the interplay between agreement/disagreement expressions and viewpoints.


One possible solution is to use supervised learning, which requires training data (Galley et al., 2004;Abbott et al., 2011;Andreas et al., 2012).

training data are also likely domain and language dependent, which makes them hard for re-use.


For example, in the above sentence, the author clearly expressed that he/she wanted to buy a car.

an example of an implicit sentence is "Anyone knows the battery life of iPhone?"


Some researchers also used topic modeling of both domains to transfer knowledge (Gao & Li, 2011;He, Lin & Alani, 2011).

none of these methods deals with the two problems/difficulties of our task.


Assuming that word probabilities are independent given a class, we have the NB classifier: The EM algorithm basically builds a classifier iteratively using NB and both the labeled source data and the unlabeled target data.

the major shortcoming is that the feature set, even with feature selection, may fit the labeled source data well but not the target data because the target data has no labels to be used in feature selection.


EM can select features only before iterations using the labeled source data and keep using the same features in each iteration.

these features only fit the labeled source data but not the target data.


The gain of iteration 1 shows that incorporating the target domain data (unlabeled) is helpful.

the selected features from source domains can only fit the labeled source data but not the target data, which was explained in Section 3.1.


Selecting features only from the target domain makes sense since it can reflect target domain data well.

it also becomes worse with the increased number of iterations, due to strong positive features.


showed that by selectively sharing parameters based on typological features of each language, substantial improvements can be achieved, compared to using a single set of parameters for all languages.

these methods all employ generative models with strong independence assumptions and weak feature representations, which upper bounds their accuracy far below that of feature-rich discriminative parsers (McDonald et al., 2005;Nivre, 2008).


We see that Delex performs well on target languages that are related to the majority of the source languages.

for languages 3 Model "D-,To" in Table 2 from Naseem et al.


As a natural first attempt at sharing parameters, one might consider forming the crossproduct of all features of Delex with all WALS properties, similarly to a common domain adaptation technique (Daumé III, 2007;Finkel and Manning, 2009).


Similarly Täckström (2012) used self-training to adapt a multi-source direct transfer named-entity recognizer  to different target languages, "relexicalizing" the model with word cluster features.

as discussed in §5.2, standard self-training is not optimal for target language adaptation.


Methods for learning with ambiguous labelings have previously been proposed in the context of multi-class classification (Jin and Ghahramani, 2002), sequence-labeling (Dredze et al., 2009), log-linear LFG parsing (Riezler et al., 2002), as well as for discriminative reranking of generative constituency parsers (Charniak and Johnson, 2005).

to Dredze et al., who allow for weights to be assigned to partial labels, we assume that the ambiguous arcs are weighted uniformly.


This is not surprising given that the base parser simply observes this phrase as DET NOUN NOUN.

looking at the arc marginals we can see that the correct analysis is available during AAST, although the actual marginal probabilities are quite misleading.


ADTree (Freund & Mason, 1999) with ten boosting iterations performed best, with 91% recall and 91% precision under 10-fold cross-validation.

the ADTree models were overfitted to the AdventureWorks domain.


This is because the most specific intelligible attribute is slightly ambiguous, and C must occasionally supply extra constraints to disambiguate.

with both specificity and vocabulary selection, L achieves a mean dialogue length of 11.3, requiring only two turns more than C to order a book.


By considering the frequency distributions from lexical category co-occurrence, they produced a set of pseudowords which were closer to real ambiguous words in terms of disambiguation difficulty than random pseudowords.

this approach requires a specific hierarchical lexicon and falls short of creating many pseudowords with high polysemy (the authors report generating pseudowords with two senses only).


We evaluate our method on existing annotated datasets from various AMT tasks.

we also want to ensure that our model can handle adversarial conditions.


Raw agreement is thus a very simple measure.

cohen's κ corrects the agreement between two annotators for chance agreement.


We also compute the κ values for each pair of annotators, and average them for each annotator (similar to the approach in Tratz and Hovy (2010)).

whenever one label is more prevalent (a common case in NLP tasks), κ overestimates the effect of chance agreement (Feinstein and Cicchetti, 1990) and penalizes disproportionately.


The previous sections showed that our model reliably identifies trustworthy annotators.

we also want to find the most likely correct answer.


Increasing the required majority also improves accuracy, although not as much, and the loss in coverage is larger and cannot be controlled.

our method allows us to achieve better accuracy at a smaller, controlled loss in coverage.


Annotators, and Supervision Adverse Strategy We showed that our model recovers the correct answer with high accuracy.

to test whether this is just a function of the annotator pool, we experiment with varying the trustworthiness of the pool.


The results of the various models are presented in table 5; multiplicative represents Mitchell and Lapata's (2008)   In the contextualized version of the similarity task (in which the landmark is combined with subject and object), all three models obtain the same result (.32).

in the non-contextualized version (in which only the target verb is combined with subject and object), the models differ in performance.


Twitter offers an unprecedented advantage on live reporting of the events happening around the world.

summarizing the Twitter event has been a challenging task that was not fully explored in the past.


Most previous summarization studies focus on the well-formatted news documents, as driven by the annual DUC 2 and TAC 3 evaluations.

the Twitter messages (a.k.a., tweets) are very short and noisy, containing nonstandard terms such as abbreviations, acronyms, emoticons, etc.


Although coherence has been used in ordering of summary sentences (Section 2.2), this work is limited by the quality of summary sentences given as input.

g-FLOW incorporates coherence in both selection and ordering of summary sentences.


In general, the normalizer of p w and the expectation over p w cannot be computed directly, since there may be exponentially many coherent subsets of anchored rules.

we note that A and its corresponding S(A) form a segmentation of the base form b, with features decomposing over individual segments.


As a concrete example of an error our model does make, Löwe (lion) is incorrectly predicted to have the first suffix, instead of the correct suffix (not shown) which adds an -n for accusative, genitive, and dative singular as well.

making this prediction correctly is essentially beyond the capacity of a model based purely on orthography.


In fact, our model induces at most 4-6 roles (even if |R| is much larger).

bayes predicts more than 30 roles for the majority of frequent predicates (e.g., 43 roles for the predicate include or 35 for say).


Ideally, two predicates can only align when their arguments are coreferent.

in practice we may incorrectly resolve argument links, or there may be implicit arguments that do not appear as syntactic dependencies of the predicate trigger.


MRR is 4 For both rank accuracy and MRR, a perfect score is 100.

mRR places more emphasis on ranking items close to the top of the list, and less on differences in ranking lower in the list.


Prior research has demonstrated that joint prediction alleviates error propagation inherent in pipeline architectures, where mistakes cascade from one task to the next (Bohnet et al., 2013;Tratz, 2013;Hatori et al., 2012;Zhang et al., 2014a).

jointly modeling all the processing tasks inevitably increases inference complexity.


As shown in section 5, syntactic features greatly improve semantic parsing.

it is interesting to explore more precisely what kind of syntactic information boosts or penalizes our predictions.


The use of paths and of the output of a graph-based parser (Bohnet, 2010) favors the capture of complex dependencies and enhances the learning of these constructions for our local transition-based parser.

we also observe that the features are not able to completely stop the loss of F 1 -score for longer sentences.


Yet, compared to stateof-the-art systems, our results built on the S&T parser score lower than the top performers (Table  10).

we are currently extending a more advanced lattice-aware transition-based parser (DSR) with beams (Villemonte De La Clergerie, 2013) that takes advantage of cutting-edge techniques (dynamic programming, averaged perceptron with early updates, etc.


Furthermore, the gain brought by PAS DM (T.PARSER+features, this paper) 92.11 89.70 (Du et al., 2014) 92.04 89.40 (Martins andAlmeida, 2014) 91.76 89.16 (DSR, this paper) 90.13 85.66    the second-order features is reduced by half when used jointly with our feature set (+1.09 vs +0.57 with them).

although we could assess that the need of second order models is thus alleviated, the conjunction of both types of features still improves the parser performance by an overall gain of 1.62 points on DM (1.18 on PAS), suggesting that both feature sets contribute to different types of "structures".


In fact, past research mentioned in Section 2 refers to this exact task as both citation prediction and link prediction.

link prediction is a commonly used phrase which may be used to describe other problems not concerning documents and citation prediction.


This cursory, qualitative critique of the metrics warrants more research, ideally with humanevaluation.

one can see how these metrics differ: TC and PMI are both entirely concerned with just the co-occurrence of terms, normalized by the general popularity of the said terms.


In this scenario, we have access to both engineered and log data to train a model.

we do not have access to web search click log data.


A simple remedy is to use word bi-grams in addition to unigrams (Blitzer et al., 2007;Glorot et al., 2011;Wang and Manning, 2012).

use of word n-grams with n > 1 on text categorization in general is not always effective; e.g., on topic categorization, simply adding phrases or n-grams is not effective (see, e.g., references in (Tan et al., 2002)).


Algorithm 3: GETPOSSIBLEACTIONS for full tree linearization, where C is a full tree Input: A state s = ([σ|j i], ρ, A) and gold tree C Output: A set of possible transition actions Algorithm 2 can also be used with full-tree constraints, which are a special case of partial-tree constraints.

there is a conceptually simpler algorithm that leverages full-tree constraints.


those results are not comparable with ours for the following reasons.


The results in this table are satisfying: with respect to LSI document similarity, our model outperforms all of the baselines and its value is close to the one achieved by human-generated headlines.

the human evaluations are middling: the word-graphs method produces more readable headlines, but our model proves to be more informative because it does better work at detecting abstract word relationships in the text.


We cannot use keyword spotting if the goal is to align instructional text to videos.

if our goal is just to create a labeled corpus of video clips, keyword spotting is a reasonable approach.


DSMs have been very effectively applied to a variety of semantic tasks (Clark, 2015;Mikolov et al., 2013b;Turney and Pantel, 2010).

compared to human semantic knowledge, these purely textual models, just like traditional symbolic AI systems (Harnad, 1990;Searle, 1984), are severely impoverished, suffering of lack of grounding in extra-linguistic modalities (Glenberg and Robertson, 2000).


Typically, each wetlab experiment has a protocol written in natural language, describing the sequence of steps necessary for that experiment.

these instructions are often incomplete, and do not spell out implicit assumptions and knowledge, causing the results to be difficult to reproduce (Begley and Ellis, 2012).


The feature weights learned by LSSVM and its variants were smaller than that for LSP (due to regularization).

they always resulted in the same forced decoding alignments in our experiments, and obtained same alignment accuracy.


Content analysis, a widely-applied social science research method, is increasingly being supplemented by topic modeling.

while the discourse on content analysis centers heavily on reproducibility, computer scientists often focus more on scalability and less on coding reliability, leading to growing skepticism on the usefulness of topic models for automated content analysis.


Until recently, the goal of training open-domain conversational systems that emulate human conversation has seemed elusive.

the vast quantities of conversational exchanges now available on social media websites such as Twitter and Reddit raise the prospect of building data-driven models that can begin to communicate conversationally.


good job in explaining away 1-gram and 2-gram message matches (Figure 4 (a)) and the performance gain mainly comes from context matches.

we observe that 4-gram matches may be important in selecting appropriate responses.


Both systems are rule-based, rather than data-driven, and do not train or test their systems with real-world portmanteaux.

to these approaches, this paper presents a data-driven model that accomplishes (2) by blending two given words into a portmanteau.


Since P (z, x, y) is relatively low compared to the step probabilities, this method prefers very short alignments-the reverse of the effect seen in the conditional method.

the model can also zero out the probabilities of unlikely aligments, so overall it learns fewer possible alignments between phonemes.


This "mixed" method, like the joint method, is more conservative in learning phoneme alignments.

like the conditional method, it has high alignment probabilities and prefers longer alignments.


Some hypotheses, like "architecology" and "japanglish," might even be considered superior to their gold counterparts.

some errors, like "js" and "mman," are clearly unacceptable system outputs.


Paraphrases are usually defined as "meaning equivalent" words or phrases.

many paraphrases, even while capturing the same meaning overall, display subtle differences which effect their substitutability (Gardiner and Dras, 2007).


A common theme across all these settings requires addressing two difficulties in linking decisions: matching the textual name mention to the form contained in the knowledge base, and using contextual clues to disambiguate similar entities.

all of these studies have focused on written language, while linking of spoken language remains untested.


al to identify explanatory text in candidate answers perform better than our approach, which relies solely on lexicalized alignment.

we expect that our two approaches are complementary, because they address different aspects of the QA task (structure vs. similarity).


These attempt to find parameter settings for which unnormalized model scores approximate the true label probability.

the theoretical properties of such techniques (and of self-normalization generally) have not been investigated.


This is somewhat surprising, since intuitively, ToBI should be capturing information very similar to what pauses and word durations capture, particularly when it is predicted based partially on these phenomena.

our learned ToBI predictor only gets roughly 50 F 1 on break prediction, so ToBI prediction is clearly a hard task even with sophisticated features.


The SIU-model has previously been applied to two datasets from the Pentomino domain , where the speaker's goal was to identify one out of a set of tetris-like (but consisting of five instead of four blocks) puzzle pieces.

in these datasets, the references were "one-shot" and not embedded in longer dialogues, as is the case in the REX corpus.


Traditionally, text categorization and NER approaches are based on textual information only.

information in visually rich formats such as PDF and HTML is often conveyed by a combination of textual and visual features.


As a result, the feature space of NER and text categorization involves purely textual features: word attributes and characteristics, their contexts and frequencies.

textual information in visually rich formats, such as PDF and HTML, is interlaced with typographic and other visually salient characteristics.


Flyers contain a single list-ing, which in turn has a single address.

broker information and space information are multivalue attributes.


Lastly, it should be noted that an overall system performance baseline is one that measures the average performance of data entry staff in commercial real estate listing services.

the terms and conditions of most listing services prohibit gathering and using data for such purposes.


If an English speaker was asked to simply choose one of the Chinese translations, they likely could not decide which is correct.

if they were additionally given English T2 translations corresponding to each of the Chinese translations, they could easily choose the third as the most natural, even without knowing a word of Chinese.


It can be noted that this formalism is a relatively simple expansion of standard SCFGs.

the additional targets require non-trivial modifications to the standard training and search processes, which we discuss in the following sections.


This limit is generally imposed by ordering rules by the phrase probability P (α 1 |γ) and only using the top few (in our case, 10) for each source γ.

in the MSCFG case, this is not so simple.


One of the weaknesses of current supervised word sense disambiguation (WSD) systems is that they only treat a word as a discrete entity.

a continuous-space representation of words (word embeddings) can provide valuable information and thus improve generalization accuracy.


• Surrounding words: Additionally, the surrounding words of a target word (after removing stop words) are also used as features in IMS.

unlike POS tags, the words occurring in the immediately adjacent sentences are also included.


Thus the selected development sets are not proper representatives of the test sets and the tuning process results in overfitting the parameters (window sizes) to the development sets, with low generalization accuracy.

per-task tuning is relatively stable and performs better on the test sets.


For example, on SE2, the improvement achieved on verbs is much larger than the other two POS types and on SE3, adjectives benefited from word embeddings more than nouns and verbs.

this    Finally, we evaluated the effect of word embeddings and the adaptation process.


For example, in the sentence "the winning goal came with less than a minute left to play 2 ", the sense tag for word 'goal' is 'goal%1:04:00::'.

the training data for IMS does not contain any sample with this sense tag and so it is impossible for IMS to assign this tag to any test instances.


Without a language model the greedy DBRNN decoding procedure loses relatively little in terms of CER as compared with the DBRNN+NN-3 model.

this 3% difference in CER translates to a 16% gap in WER on the full Eval2000 test set.


For transition-based dependency parsers, the feature context for a parsing state is represented by the neighboring elements of a word token in the stack containing the partial parse or the buffer containing unprocessed word tokens.

in our treeto graph parser, as already stated, buffers σ and β only specify which arc or node is to be examined next.


(7)) are similar to the LP-MERT constraints (5), although with the addition of slack variables and the ∆ function to handle infeasible solutions.

if a feasible solution is available for MIRA, then these extra quantities are unnecessary.


However, if we consider the normal fan as a whole we can clearly see that w ∈ N {h i } is the optimal point under the regularisation.

it is not obvious in the projected parameter space thatŵ is the better choice.


Some systems focus on syntactic generation (Bangalore and Rambow, 2000;Langkilde-Geary, 2002;Filippova and Strube, 2008) or linearization and inflection (Filippova and Strube, 2007;He et al., 2009;Wan et al., 2009;Guo et al., 2011a), and avoid thus the need to cope with this projection all together; some use a rule-based module to handle the projection between non-isomorphic structures (Knight and Hatzivassiloglou, 1995;Langkilde and Knight, 1998;Bohnet et al., 2011); and some adapt the meaning structures to be isomorphic with syntactic structures (Bohnet et al., 2010).

it is obvious that a "syntacticization" of meaning structures can be only a temporary workaround and that a rule-based module raises the usual questions of coverage, maintenance and portability.


Such research on temporal text analysis generally focuses on determining when events start and end or how they relate temporally to each other; specific goals include information extraction of time-dependent facts from news media (Ling and Weld, 2010;Talukdar et al., 2012), or extracting personal histories in social media (Wen et al., 2013).

our goal is to find the temporal orientation of people.


For instance, Twitterspecific tokens (e.g., retweets) are often removed during normalization, so examining the removal of these words as a group is warranted.

these tokens are never added, so different segmentation is appropriate when examining word addition.


Note that since the first measure is one of overall performance, smaller numbers reflect larger performance drops when removing a given type of edit, so that the smaller the number the more critical the need to perform the given type of normalization.

the latter judgment is one of error rate, and thus interpretation is reversed; the larger the error rate when it is removed, the more critical the normalization edit.


Perhaps unsurprisingly, failing to add subjects and verbs resulted in the largest issues, as the parser has little chance of identifying these dependencies if the terms simply do not appear in the sentence.

not all word additions proved crit-  ical, as failing to add in a missing determiner generally had little impact on the overall performance.


Similar to those on dependency parsing, the results on speech synthesis suggest that a broad approach that considers several different types of normalization edit is necessary to produce results comparable to those seen on clean text.

at a high level there is a clear divide in importance between normalization types, where the greatest performance gains can be obtained by focusing on the comparatively small number of token removals.


While normalization for speech synthesis is primarily dependent on removing unknown tokens, normalization that targets name entity recognition would be better served focusing on replacing non-standard to-kens with their standard forms.

parsertargeted normalization must attend to both of the tasks, as well as the task of restoring dropped tokens.


Alternatively, you may argue in favor of the death penalty because it gives victims of the crimes closure.

you may argue against the death penalty because some innocent people will be wrongfully executed or because it is a cruel and unusual punishment.


For example, one author may believe that the death penalty is a cruel and unusual punishment while the other one attacks that position.

in order to attack that position they must be discussing the same facet.


For the first batch we randomly selected 500 pairs from our pairs dataset of 1131 pairs.

our subsequent impression was that the clustering had not filtered out enough of the unrelated pairs (score 0-1).


This biases the distribution of the training set to having a much larger set of more similar pairs, which has been a problem for previous work (Boltuzic andŠnajder, 2014), where the vast majority of pairs that were labelled were unrelated.

the AFS task is clearly different than STS, partly because the data is dialogic and partly because it is argumentative.


Another parallel may exist between work on nuclearity in RST and its use in summarization (Marcu, 1999).

our notion of a CENTRAL PROPOSITION is different than nuclearity in RST, since FACETS are derived from CEN-TRAL PROPOSITIONS that rise to the top of the pyramid across summarizers, and then (via AFS) across many dialogs on a topic, while RST nuclearity is only defined for a span of text by a single speaker.


Emotional information has been observed even in summaries of professional chats discussing technology (Zhou and Hovy, 2005).

the instructions to our Pyramid annotators were to not include information of this type in the pyramids.


Even though LwR provides huge benefits, providing both a label and a rationale is expected to take more time of the labeler than simply providing a label.

the improvements of LwR over Lw/oR is so huge that it might be worth spending the extra time in providing rationales.


Context representations are a key element in distributional models of word meaning.

to typical representations based on neighboring words, a recently proposed approach suggests to represent a context of a target word by a substitute vector, comprising the potential fillers for the target word slot in that context.


Finally, we rank the candidates using the scores in our in-context paraphrase vectors from Section 6.2.

this time we check the effect of injecting a stronger bias towards the given context c, by averaging only the top-m percent contexts most similar to c, for m ∈ {1%, 5%, 10%, 100%}, as described in Section 4.2.


Then, while processing all instances of the same word type u one after the other, only the substitute vectors in C u need to be loaded into memory.

in an 'online' mode, to be ready for any arbitrary word instance input, our model would need to keep in memory substitute vectors for all the word types in the vocabulary V .


(2013) use the Web as their corpus, and de Melo & Bansal use Google N-grams (Brants and Franz, 2006).

this results in a large number of instances where satisfied lexical patterns do not correspond to adjectives (e.g., sometimes but not always).


They make use of standard context vectors for clustering adjectives, where context for every adjective comprises of nouns it modifies across all sentences in a corpus.

recent work shows promise for context vectors embedded in a compressed semantic space that are derived using neural networks: Baroni et al.


Given a window size w, the CBOW model predicts the current word given the neighboring words as context.

the skip-gram model predicts the neighboring words given the current word.


Zero pronoun resolution, like general pronoun resolution, is almost universally approached as a problem of linking a pronoun to an overt noun phrase antecedent in the text.

while some zero pronouns do have overt noun phrase antecedents, many other zero pronouns do not.


3h 9) Teacher Ø 还在面试阶段吗 Are you still in the interview phase?

2v there are plenty of utterances (e.g., Table 1 lines 4 and 6) in which the English translation does not contain an overt subject.


In most previous work on KB completion to predict missing relation facts (Mintz et al., 2009;, the methods are evaluated on a subset of facts from a single KB snapshot, that are hidden while training.

given that the missing entries are usually selected randomly, the distribution of the selected unknown entries could be very different from the actual missing facts distribution.


One might expect that with the increased number of types, the embedding model would perform better than the classifier since they share parameters across types.

despite the recent popularity of embedding models in NLP, linear model still performs better in our task.


The pronunciation of Spanish words is recoverable from the spelling by applying a limited set of rules (Kominek and Black, 2006).

there is some ambiguity in the opposite direction; for example, the phoneme [b] can be expressed with either 'b' or 'v'.


For example, the word amputate is listed as monomorphemic, but in fact contains the suffix -ate.

amputee is analyzed as amputee = amputate − ate + ee.


The LCCT method achieves a relative high accuracy with 10% of the reviews labeled, better than SVM, TSVM and Self-learning with 100% of the reviews labeled.

when all the training data are labeled, LCCT is still significantly more accurate than all the competitors except Nguyen's method.


Various other researchers have tried to improve the performance of their paraphrase systems or vector space models by using diverse sources of information such as bilingual corpora (Bannard and Callison-Burch, 2005;Huang et al., 2012;Zou et al., 2013), 10 structured datasets (Yu and Dredze, 2014; or even tagged images (Bruni et al., 2012).

most previous work 11 did not adopt the general, simplifying view that all of these sources of data are just cooccurrence statistics coming from different sources with underlying latent factors.


We know that whenever P > 0.5, the error rate decreases (and therefore Acc increases) so the text is improved.

3 an increase in P , R or F alone does not necessarily imply an increase in Acc or WAcc, as illustrated in Table 8.


Therefore, we must accept that any metric used in such scenarios will not be perfect.

it is worth noting that this limitation does not extend to evaluation of error detection per se using such metrics.


Recording speech poses no particular problems, but retrieval of spoken content using spoken queries is presently available only for the approximately two dozen languages in which there is an established path to market; English, German, or Chinese, for example.

many of the mobile-only users who could benefit most from such systems speak only one of the several hundred other languages that each have at least a million speakers; 1 Balochi, Mossi or Quechua, for example.


Without multiple draws, confidence intervals on this value cannot be established.

we are confident that random baselines even as high as 0.1 for any of our measures would be surprising.


Thus, even if we somehow adapt ABA's product-of-marginals heuristic to such models, we run the risk of estimating highly inaccurate posteriors (specifically, zero-valued posteriors).

mIR extends to all IBm-style word alignment models and does not add heuristics.


On both language pairs, ABA, PostCAT and MIR outperform their respective EM baseline with comparable gains overall.

we noticed that ABA and MIR are not producing the same alignments.


For evaluating slot induction (AP and PR-AUC), the double-graph random walk (row (e)) performs better on both ASR and manual results, which implies that additionally integrating the lexical knowledge graph helps decide a more coherent and complete slot set since we can model the score propagation more precisely (not only slot-level but wordlevel information).

for SLU evaluation (WAP and AF), the single-graph random walk (row (c)) performs better, which may imply that the slots carrying the coherent relations from the row (e) may not have good semantic decoder performance so that the performance is decreased a little.


The difficulty lies in the manner of distinguishing paraphrases from expressions that stand in different semantic relations, e.g., antonyms and sibling words, using only the statistics estimated from such corpora.

highly accurate paraphrases can be extracted from parallel or comparable corpora, but their coverage is limited owing to the limited availability of such corpora for most languages.


(3) a. amendment of regulation ⇔ amending regulation b. amendment of X ⇔ amending X c. amendment of documents ⇔ amending documents Using that method, they were able to expand the seed lexicon by a large multiple (15 to 40 times), and the new paraphrase pairs were of reasonably good quality.

they introduced variables only for identical word forms shared by both sides of each pair and left corresponding pairs of lexical variants, e.g., ("amendment", "amending") in (3a), untouched.


However, these scores are reasonably high, considering that no use is made of rich language-specific resources 12 .

more grammatical errors occurred than with S Seed and S ID .


Training in machine learning often uses starting big which is to use up all training data at the same time.

elman (1993) suggests that in some cases, learning should start by training simple models on small data and then gradually increase the model complexity and add more difficult data.


MaxEnc strategy helps the system achieve the highest accuracy on short sentences (up to length 10).

it is less helpful than Mi-nEnc when performing on long sentences.


It shows that lexical semantics plays a decisive role in the performance of the system.

it is worth noting that, even without that knowledge (i.e., with the ∞-order generative model alone), the DDA of phase 1 is 2% higher than before being trained (66.89% vs 64.9%).


The two-registers system is not quite arc-decomposable (Goldberg and Nivre, 2013): if the wrong vertex is stored in a register then a later pair of crossed arcs might both be individually reachable but not jointly reachable.

there may be a "crossing-sensitive" variant of arcdecomposability that takes into account the vertices crossed arcs are incident to that would apply here.


Xiao and Guo (2013) induce word embeddings across multiple domains, and concatenate these representations into a single feature vector for labeled instances in each domain, following EasyAdapt (Daumé III, 2007).

they do not apply this idea to unsupervised domain adaptation, and do not work in the structured feature setting that we consider here.


Our formulation is related to multi-domain learning, particularly in the multiattribute setting.

rather than partitioning all instances into domains, the domain attribute formulation allows information to be shared across instances which share metadata attributes.


We were surprised that SG-EM+RETRO actually performed worse than SG-EM, given how poorly SG-EM performed in the other evaluations.

an analysis again revealed that this was due to the kind of similarity encouraged by WordNet rather than an inability of the model to learn useful vectors.


(2014) introduce an approach that chooses between multiple composition tensors (AdaMC-RNTN), which yields further gains with respect to RNTN performance.

to the lexicalized and highdimensional RNTN model, there are several lines of work that attempt to work in a more data-scarce setting.


Since Brown clusters are mostly syntactic/semantic in nature and do not automatically distinguish positive or negative sentiment, we additionally performed multiple experiments to use clusters while incorporating additional sentiment information: On one hand, we try to incorporate the judgements on the Amazon near-domain dataset more directly into the clusters by using the repeated bisecting K-Means algorithm as implemented in CLUTO (Zhao and Karypis, 2005), with previous/next word, part-of-speech tag, and the score of the containing review as features.

we split the Brown clusters according to the sentiment value that they have in a particular sentiment lexicon (e.g.


The similarity is computed based on statistics such as co-occurrence which are unable to accommodate the subtlety that whether two words labeled as similar are truly similar depends on which topic they appear in, as explained by the aforementioned examples.

ideally, the knowledge would be word A and B are similar under topic C. in reality, we only know two words are similar, but not under which topic.


DF-LDA and Quad-LDA proposed to use word correlations to enhance the coherence of learned topics.

they improperly enforce words labeled as similar to have similar probabilities in all topics, which violates the fact that whether two words are similar depend on which topic they appear in.


Topic models provide insights into document collections, and their supervised extensions also capture associated document-level metadata such as sentiment.

inferring such models from data is often slow and cannot scale to big data.


2 Typically, the topics are discovered through a process of probabilistic inference, either variational EM (Wang et al., 2009) or Gibbs sampling (Boyd-Graber and Resnik, 2010).

these methods scale poorly to large datasets.


While these advancements improve the scalability of max-margin supervised topic models, the improvement is limited by the fact that the sampling algorithm grows with the number of tokens.

this paper explores a different vein of research that focuses on using efficient representations of summary statistics to estimate statistical models.


Traditional information extraction would be content with extracting two binary relation instances (NEG-REG,BCL,RFLAT) and (NEG-REG,IL-10,RFLAT), where NEG-REG represents a negative regulation (i.e., inhibition).

the sentence also discloses important contextual information, i.e., BCL regulates RFLAT by stimulating the inhibitive effect of IL-10, and likewise the inhibition of RFLAT by IL-10 is controlled by BCL.


Complex knowledge extraction can be naturally framed as a semantic parsing problem, with the event structure represented by a semantic parse; see Figure 2.

annotating example sentences is expensive and time-consuming.


(2014) took an important step toward this direction, by learning a semantic parser based on combinatorial categorial grammar (CCG) from Freebase and web sentences.

krishnamurthy and Mitchell (2012) still learned from binary relations, using only simple sentences (of length ten or less).


Automatic sentiment analysis of text, especially social media posts, has a number of applications in commerce, public health, and public policy development.

a vast majority of prior research on automatic sentiment analysis has been on English texts.


Thus, instead of building source-language specific sentiment analysis systems, one can translate the texts into English and use an English sentiment analysis system.

it is widely believed that aspects of sentiment may be lost in translation, especially in automatic translation.


This suggests that certain attributes of automatically translated text mislead humans with regards to the true sentiment of the source text.

these same attributes do not seem to affect the automatic sentiment analysis system as much.


The LM score features are extracted from large amounts of news article data, and are good representation of the general importance of bigrams for the test domain.

word-Net information is collected from a more general aspect, which may not be a very good choice for this task.


By the constraints of the algorithm, a head word x h must combine with each of its left and right dependents.

the order of combination can lead to different tree structures (as illustrated in Figure 2).


The scoring function requires specifying a set of parse features f which, in theory, could be directly adapted from existing lexicalized c-parsers.

the structure of the dependency parse greatly limits the number of decisions that need to be made, and allows for a smaller set of features.


This task effectively amounts to disambiguating the sense of discourse connective, which can be done with high accuracy (Pitler et al., 2008).

in the absence of an explicit discourse connective, inferring the sense of a discourse relation has proved to a very challenging task (Park and Cardie, 2012;Rutherford and Xue, 2014).


The common approach has been to inject knowledge as features.

these pieces of knowledge provide relatively strong evidence that loses impact in standard training due to sparsity.


As hard coreference problems are rare in standard coreference datasets, we do not have significant performance improvement.

these results show that our additional Predicate Schemas do not harm the predictions for regular mentions.


With the development of machine learning based models (Connolly et al., 1994;Soon et al., 2001b;Ng and Cardie, 2002a), attention shifted to solving standard coreference resolution problems.

many hard coreference problems involve pronouns.


Mnih and Teh (2012) fix these parameters to 1 and obtain the same perplexities, thereby circumventing the need for explicit normalisation.

this method does not provide any guarantees that the models are normalised at test time.


One option for speeding up factored models is using a GPU to perform the vector-matrix operations.

gPU integration is architecture specific and thus against our goal of making our language modelling toolkit usable by everyone.


We obtain a slightly better BLEU score with stochastic gradient descent, but this is likely to be just noise from tuning the translation system with MERT.

noise contrastive training reduces training time by a factor of 7.


As a first attempt to automate the test, we only experiment with basic linguistic features.

we believe that the task itself offers an opportunity for the development of-and subsequent evaluation of-rich linguistic features that may be better equipped for determining the aboutness of conversations.


A portion of user utterances refer to general Java knowledge, and in these cases semantic interpretation can be accomplished by mapping to a domain-specific ontology (e.g., Dzikovska et al., 2007).

many utterances refer to concrete entities within the dynamically changing, user-created programing artifact.


Importantly, this value does not test generalization to unseen questions, since KNOWBOT has held dialogs on these questions.

it does show that our system can effectively learn about its domain: a poor dialog extraction system will fail to extract any helpful knowledge from users during a training dialog.


Our open system acquires relations from a wide variety of user explanations without the bottleneck of a hand-built dialog model, but the tradeoff is that we use relatively simple, templated system prompts.

our collected corpus of real human-system dialogs can be used to improve our system in further iterations.


This is to be expected, as the speech in the control group has more complete sentences and fewer disfluencies.

it is interesting to note that performance on the PNFA and SD groups is not much worse.


All the features relating to Yngve depth and height of the parse trees are significantly different (in at least one of the three clinical groups).

of the eight primary syntactic units calculated by Lu's SCA, six show no significant difference when measured on the automatically segmented transcripts.


Our analysis so far suggests that some syntactic units are relatively impervious to the automatic sentence segmentation, while others are more susceptible to error.

when we examine the mean values given in Table 3, we observe that even in cases when the complexity metrics are significantly different in the automatic transcripts, the differences appear to be systematic.


For the sake of example, annotator A5 in the CONFLICT setting will annotate documents with the true class B as B exactly 10% of the time but might annotate B as C 85% of the time.

annotator A4 might annotate B as D most of the time.


In information extraction, for example, one needs to predict the relation labels y that an entity-pair x can have based on the hidden relation mentions h, i.e., the relation labels for occurrences of the entity-pair in a given corpus.

these models are often trained by optimizing performance measures (such as conditional log-likelihood or error rate) that are not directly related to the task-specific non-linear performance measure, e.g., the F 1 -score.


However, these models are often trained by optimizing performance measures (such as conditional log-likelihood or error rate) that are not directly related to the task-specific non-linear performance measure, e.g., the F 1 -score.

better models may be trained by optimizing the taskspecific performance measure while allowing latent variables to adapt their values accordingly.


The goal of our learning problem is to find w ∈ R d which minimizes the expected loss, aka risk, over a new sample D of size N : Generally, the loss function ∆ cannot be decomposed into a linear combination of a loss function δ over individual training samples.

most discriminative large-margin learning algorithms assume for simplicity that the loss function is decomposable and the samples are i.i.d.


We will show below that this pretraining is critical for getting good performance in the paraphrase task.

the general design principle of this type of unsupervised pretraining should be widely applicable given that next-word prediction training is possible in many NLP applications.


Parsing is a potentially powerful tool for identifying the important meaning units of a sentence, which can then be the basis for determining meaning equivalence.

reliance on parsing makes these approaches less flexible.


These vectors can then be used to perform query classification or web search.

to existing representation learning methods which employ either unsupervised or single-task supervised objectives, our model learns these representations using multi-task objectives.


In the following, we elaborate the model in detail: Word Hash Layer (l 1 ): Traditionally, each word is represented by a one-hot word vector, where the dimensionality of the vector is the vocabulary size.

due to the large size of vocabulary in realworld tasks, it is very expensive to learn such kind of models.


For such languages, many forms will not be attested even in a large corpus.

different lemmas often exhibit the same inflectional patterns, called paradigms, which are based on phonological, semantic, or morphological criteria.


Allomorphic representations have the potential advantage of reducing the complexity of transductions by the virtue of being similar to the correct form of the affix.

we found that allomorphic affixes tend to obfuscate differences between distinct inflections, so we decided to employ abstract tags instead.


Our Basic model outperforms DDN for both nouns and verbs, despite training on less data.

reranking actually decreases the accuracy of our system on Czech nouns.


A popular surrogate is an 1 penalty, Ω(θ) def = w |θ w |.

1 would not recognize that θ is simpler with the features {ab, abc, abd} than with the features {ab, pqr, xyz}.


They report superior performance of their hybrid model over both component models.

their model does not consider the coherence of the target word during the generation process, nor other important features that have been shown to significantly improve machine transliteration (Li et al., 2004;Jiampojamarn et al., 2010).


and  combine the pivot model with a grapheme-based model, which works better than either of the two approaches alone.

their model is not able to incorporate more than two languages.


Unfortunately, phonetic transcriptions are rarely available, especially for words which originate from other languages, and generating them on the fly is less likely to help.

transliterations from other languages constitute another potential source of information that could be used to approximate the pronunciation in the source language.


In general, compatible terms will be semantically related (dog and animal).

relatedness does not suffice: many semantically related, even very similar terms are not compatible (dog and cat).


A high accuracy was reported on a word translation task, where a word projected to the vector space of the target language is expected to be as close as possible to its translation (Mikolov et al., 2013b).

we note that the 'closeness' of words in the projection space is measured by the cosine distance, which is fundamentally different from the Euler distance in the objective function (3) and hence causes inconsistence.


The component monolingual n-gram LMs must be trained on monolingual corpora in their respective languages.

due to the lack of codified orthographic conventions concerning spelling, diacritic usage, and spacing, compounded by the liberal use of now-obsolete shorthand notations by printers, statistics gleaned from available modern corpora provide a poor representation of the language used in the printed documents.


Our system, as currently designed, attempts to faithfully transcribe text.

for the purposes of indexability and searchability of these documents, it may be desirable to also produce canonicalized transcriptions, for example collapsing spelling variants to their modern forms.


Citation sentences (citances) to a reference article have been extensively studied for summarization tasks.

citances might not accurately represent the content of the cited article, as they often fail to capture the context of the reported findings and can be affected by epistemic value drift.


Such improvement is expected, as UMLS-expand augments the citance with all possible formulations of the detected biomedical concepts.

its precision is only comparable with the baseline, as it does not remove any noisy terms from the citance.


These two approaches can, of course, be combined.

to our knowledge, the issues of how to combine the approaches and when that is likely to be useful have not been thoroughly studied.


We 9 We are not aware of an appropriate significance test for experiments where subsets of the training data are used.

the benefits of stacking seem unlikely to be due to chance.


Simply combining the outputs from a timeline generation system and a comment summarization system may lead to timelines that lack cohesion.

articles and comments are from intrinsically different genres of text: articles emphasize facts and are written in a professional style; comments reflect opinions in a less formal way.


An exception is the unsupervised model of Guinaudeau and Strube (2013) (G&S), which converts the document into a graph of sentences, and evaluates the text coherence by computing the average out-degree over the entire graph.

despite the apparent success of these methods, they rely merely on matching mentions of the same entity, but neglect the contribution from semantically related but not necessarily coreferential entities.


For example, the text in Figure 1a 1 has no common entity in s 2 and s 3 .

the transition between them is perfectly coherent, because there exists close semantic relatedness between two distinct entities, Gates in s 2 and Microsoft in s 3 , which can be captured by the world knowledge that Gates is the person who created Microsoft (represented by Gates-create-Microsoft).


For a given pair of entities in the text, the chance is rather low to find instances in the knowledge bases where the two arguments perfectly match the pair of entities, because entities in the source document might appear in aliases or abbreviations.

partial matching between arguments and entities usually increases coverage but at risk of introducing more noise.


This representation captures not only the distribution information of individual entities but also the semantic relatedness between different entities.

the original graph-based model by G&S (Figures 2a and  2b) includes common-entity edges only and misses the semantic relatedness information.


As mentioned previously, numerous extensions have been proposed to the original entity-based model of B&L.

those extensions mostly rely on entity matching and thus fail to incorporate the information from semantically related yet distinct entities.


Intuitively, Feature 1 is a recall-enhancing feature: it encodes a condition whose satisfaction can help discover many event coreference links.

it is not designed to be precision-oriented, as it is computed based solely on the triggers and not their surrounding contexts.


newswire data, the primary source of existing coreference data, lack the richness necessary to truly solve coreference.


We argue in Section 2 that to truly solve coreference resolution, the research community needs high-quality datasets that contain many challenging cases such as nested coreferences and coreferences that can only be resolved using external knowledge.

newswire is deliberately written to contain few coreferences, and those coreferences should be easy for the reader to resolve.


Rule-based extractors, on the other hand, can be easily extended to novel relations and improved for existing but inaccurate relations, through first-order formulae that capture auxiliary domain knowledge.

usually a large set of such formulae is necessary to achieve generalization.


The degree to which r s ⇒ r t is captured is quite high for all models (0.94, 0.96, and 0.97 for matrix factorization, pre-factorization inference, and joint optimization respectively).

the probability of r t ⇒ r s is also relatively high for matrix factorization and pre-factorization inference (0.81 and 0.83 respectively), suggesting that these methods are primarily capturing symmetric similarity between relations.


Specifically, approaches based on Markov Logic Networks (MLNs) (Richardson and Domingos, 2006) encode logical knowledge in dense, loopy graphical models, making structure learning, parameter estimation, and inference hard for the scale of our data.

in our model the logical knowledge is captured directly in the embeddings, leading to efficient inference.


The basic intuition is that the entity referents of m and related mentions should be similarly connected in the KB.

there might be many entity mentions in the context of a target entity mention that could potentially be leveraged for disambiguation.